How would you explain your research?

Mapping the human brain connectome, the set of connections that support information transfer between remote areas, is not only important for understanding normal brain function and cognition, but will also help tackle the even more complex pathological brain. Magnetic Resonance Imaging (MRI) uniquely offers the possibility to perform this mapping. In particular, diffusion and functional MRI have been shown to have huge potential in estimating brain connectivity. However, these approaches are indirect. Connections are inferred through complex computational modelling frameworks rather than measured. As a result, the accuracy and precision of connectivity estimates can be limited due to inherent limitations in the measurements and the modelling.

Since multiple imaging modalities aim for the same target quantity, developing frameworks that perform the estimation using all data simultaneously is expected to significantly improve the current state of the art, which independently considers diffusion and functional MRI. In this PhD project, we aim to develop such a multi-modal connectivity framework with machine learning techniques. By integrating multiple modalities, their independent sources of error will cancel out, and the complementarity in the measurements will allow better estimation. Such improvement is key in our ability to characterise individual variability and improve the quality of subject-specific predictions.

Why Nottingham and why the Precision Imaging Beacon?

The development of such multi-modal integration frameworks is very relevant to the Beacon, given the aim to map and decode the clinical connectome. It is also very timely, given the large number of initiatives on both sides of the Atlantic (the NIH Human Connectome Project, its Lifespan expansion and various Connectomes for Disease, the ERC Developing Human Connectome Project and the UK Biobank) that collect diffusion and functional MRI to map the connectome. Given my supervisors’ strong links with these initiatives, the developed technology can have immediate applicability and impact.

What inspired you to pursue this area?

Since I was I child, I have been interested in the mind, the way of thinking and the behaviour of people. At the same time, I started to become interested in Artificial Intelligence: that is, how to imitate or reproduce this concept of ‘mind’.

We all agree that technology should be at the service of the people. During my Bachelor’s degree, most job opportunities and work experience offered were in telecommunication, consumer technology and even the military industry. In my opinion however, issues like the environment, sustainability or the health of people take precedence.

For these reasons, I decided to dedicate my work to computational neuroscience.

MRI is the most used advanced imaging technique (both in research and in clinical practice) and with the largest pace of development

How will your research affect the average person?

Over the years, the timeframe needed to translate lab discoveries into clinical practice has considerably shortened. Fortunately, our field has two main advantages:

1. MRI is the most used advanced imaging technique (both in research and in clinical practice) and with the largest pace of development.
2. Once validated, the computational models are easily translated and implemented in clinical practice.

Therefore, developments in this area have a huge impact in society with a very low-cost trade-off. The results of these projects range from new biological findings or biomarkers for diseases, leading to the improvement of therapies and diagnostics and optimization in the cost-efficiency of everyday clinical practice. 

What’s been the greatest moment of your career so far?

I have truly enjoyed the first stages of research and work experience. My first internship in a research lab, which focused on the neuro-rehabilitation of people who have suffered a stroke or any other type of brain injury, was particularly useful. After my six-month internship there, we were able to recover/improve the capacity of movement of various patients’ limbs through electrical stimulation, training and analysis of brain data. This was how I realised clinical technology is what I want to dedicate my life’s work to.

How will being based at UoN and joining Precision Imaging help you achieve your goals?

A project like this needs to be supported by a multidisciplinary team formed by physicists, mathematicians, clinical neuroscientists and computer scientists. Both the forefront facilities in the Sir Peter Mansfield Imaging Centre and the Precision Imaging Beacon depict the vested interest the University holds in MRI and computational neuroscience. I consider this to be the perfect environment for finding success in my PhD studies.

What aspects of your research and role are you looking forward to?

The complex brain models proposed last decade have substantially improved their ability to reflect biological behaviours. However, they are still far from the major goal in neuroscience: to understand and explain how the brain works. With this project we would like to overcome one of the biggest challenges in the field currently: the multimodality integration of information. Surpassing this frontier is pinned to be an important step for future computational neuroscience development.